For online LiDAR-MOS, there are deep network-based methods, which use generic end-to-end trainable models to learn local and global statistical relationships directly from data.
For example, there are point cloud scene flow methods [1]}, [2]}, [3]}, which usually estimate motion vectors between two consecutive scans. Based on the predicted motion vectors, it separates moving and non-moving objects by estimating the velocity of every point, which may not differentiate between slowly moving objects and sensor noise.
It is worth mentioning that most of them are hard to handle large scans (about 100k points) obtained by the LiDAR sensor, and the real-time performance is difficult to guarantee.
In addition, it is also possible to determine whether the object is moving according to the displacement of the bounding box, which requires some prior information for target detection or tracking [4]}, [5]}, [6]}, [7]}, [8]}.
